Comparing CalReg performance with other multivariate methods for estimating selected soil properties from Moroccan agricultural regions using NIR spectroscopy

Rabie Reda,Taoufiq Saffaj,Hatim Derrouz,Salah Eddine Itqiq,Ilham Bouzida,Ouadi Saidi,Brahim Lakssir,El Mestafa El Hadrami

doi:10.1016/j.chemolab.2021.104277

Abstract

Nowadays, near-infrared spectroscopy (NIRS) is widely used as an alternative method for analysing soil attributes. The successful determination of soil attributes by this method depends on the selection of an appropriate multivariate calibration technique. In this study, four multivariate techniques, including partial least squares regression (PLSR), memory-based learning (MBL), backpropagation neural network (BPNN) and a new proposed method named ‘combined classification & regression method’ (CalReg) were compared in terms of accuracy in predicting soil properties, including extractable calcium (Caex), extractable iron (Feex), zinc (Zn), extractable magnesium (Mgex), extractable potassium (Kex), phosphorus extracted by Olson method (P-Olson), calcium carbonate (CaCO3), soil organic carbon (SOC), total nitrogen (TN), clay, silt, sand, cation exchange capacity (CEC), and pH. A total of 660 soil samples collected from six Moroccan agricultural regions were used as the dataset for the laboratory’s calibration and validation procedure. Prediction accuracy was quantified using four statistical metrics of accuracy including the coefficient of determination (R2), root mean squared error (RMSE), the ratio of performance to interquartile distance (RPIQ) and residual prediction deviation (RPD). Both calibration and data test sets showed that the CalReg model outperformed the other tested models for SOC, TN, Caex, CEC, P-Olson, Kex and pH predictions. At the same time, MBL outperformed the other models for Feex, Zn, silt, sand and clay prediction. Moreover, BPNN outperformed the other models for Mgex and CaCO3 predictions. The best predictions were obtained by the CalReg model for SOC (R2ts = 0.92; RMSEts = 0.29; RPD = 4.73, RPIQ = 5.71), TN (R2ts = 0.90; RMSEts = 364.80; RPD = 3.39, RPIQ = 3.16), Caex (R2ts = 0.88; RMSEts = 3.25; RPD = 3.33, RPIQ = 4.40), CaCO3 (R2ts = 0.89; RMSEts = 3.86; RPD = 3.18, RPIQ = 3.95), and clay (R2ts = 0.88; RMSEts = 3.95; RPD = 3.25, RPIQ = 4.81). Furthermore, the results showed that CalReg helps improve the accuracy of soil property prediction. NIRS combined with CalReg has great potential to determine accurately the selected soil properties in the laboratory.

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